(1) Field of Invention
The present invention relates to a system for discovering unknown patterns from data of multiple scales and, more particularly, to a system for discovering unknown patterns from data of multiple scales by combining information dynamics, value of information, and multi-scale analysis.
(2) Description of Related Art
Autonomous information-processing systems have recently become practical and feasible in many application domains due to the increased ability to collect large quantities of data from a variety of different sensors, the significant growth in computational resources available, and improved analysis algorithms. Current techniques, such as matched and specific filters, have significant limitations including: 1) being domain specific; 2) assuming homogeneous data, sensors, and granularity; 3) being limited in the ability to handle uncertain, imprecise, incomplete, missing, or contradictory data; and 4) requiring a priori knowledge of features or patterns of interest.
Information dynamics is rooted in information theory, has a well-established theoretically sound foundation, and explicitly models uncertain, incomplete, and imprecise data. Information dynamics extends information theory to compute local measures of information storage, transfer, and modification, rather than the standard global (averaged) measures typically used. In the field of information dynamics, Schreiber (see the List of Cited Literature References, Literature Reference No. 13) proposed transfer entropy as an information theoretic measure to overcome the limitations of the traditional metric of mutual information. Transfer entropy enables distinguishing between shared information and information as a result of common histories; thus, directional information flows are identified. Additionally, Lizier (see Literature Reference No. 7) developed a framework for local information dynamics consisting of various information theoretic measures, including transfer entropy. However, Lizier (see Literature Reference No. 7) does not have any framework for handling incomplete and missing data or for identification of patterns at multiple scales. Further, Bandt and Pompe (see Literature Reference No. 3) proposed permutation entropy to compare the neighboring points of data to arrive at a symbol sequence of time series without any modeling assumptions. Permutation entropy is fast, robust, and invariant to nonlinear monotonous transformations. Staniek and Lehnertz (see Literature Reference No. 15) proposed that symbolic transfer entropy essentially computes transfer entropy over transformed symbolic time series.
Each of the prior methods described above exhibit limitations that make them incomplete. The invention described herein extends the concepts of permutation entropy and symbolic transfer entropy and uses these concepts under the framework of information dynamics. The present invention combines information dynamics, value of information, and multi-scale analysis to discover unknown patterns from data of multiple scales to support automated decision making.